Measuring transfer from AI training into work

Define the behavior and transfer distance

Name the work task, expected output, authoritative sources, permitted AI contribution, material failure, and human decision. Translate the learning objective into an observable behavior such as framing the audience, checking a material claim, reconciling sources, challenging AI output, or escalating uncertainty.

State how far the transfer must travel. Near transfer applies a behavior to a similar case; far transfer applies it across changed context, tools, evidence, or consequence. Do not report a classroom retest as proof of performance in ordinary work.

The UK government's 2026 employer guide to AI upskilling emphasizes practical, contextualized, integrated, modular, reachable, expandable, and sustainable learning. It provides current programme-design evidence while local transfer still requires direct observation.

Sources: [1]

Build the transfer chain ledger

For each behavior, record the teaching activity, controlled practice, initial evidence, workplace trigger, opportunity present or absent, later work artifact, process evidence, manager condition, environmental blocker, reviewer anchor, observed result, confidence, elapsed time, and decision to reinforce, redesign, expand, or hold.

Use comparison cases that preserve the construct without repeating the answer. Keep not observed separate from not demonstrated. If a participant lacked source access, time, a usable interface, or an escalation route, record the condition before attributing the result to capability.

Define the transfer window before seeing results. Immediate, delayed, and sustained evidence answer different questions. If later observation is unavailable, report the chain as incomplete rather than carrying an early result forward. Preserve changes in task difficulty and manager support.

Choose the programme decision in advance. A transfer review might change a module, add coached practice, repair work conditions, or limit an expansion. Without a named decision, the ledger can become a collection of interesting observations with no accountable response.

The OECD's Building an AI-ready public workforce connects AI capability with governance, leadership, strategic alignment, organizational conditions, and continuous learning. Its public-sector findings inform transfer questions but do not define a universal individual benchmark.

Sources: [2]

Worked artifact: a synthetic forecast-commentary cohort

Imagine a synthetic finance training cohort learning to trace AI-generated statements to approved planning assumptions. During training, participants correct a commentary draft that mixes an approved forecast with an unapproved sales proposal. The ledger records the source comparison and revision.

Two weeks later, a different synthetic case contains a new pricing assumption, an older budget file, and a message describing an optimistic scenario. One participant links every material claim to the current assumption and labels the scenario unresolved. Another produces a polished paragraph using the older budget file.

The first record supports near-transfer evidence for source authority in a comparable task. The second identifies a reinforcement hypothesis, not a stable personal label. A third participant cannot open the planning workbook, so the opportunity is marked compromised rather than failed.

The programme owner adds manager-led source checks and schedules a later case with a different workflow. The case is synthetic, and results do not establish durable workplace impact, business value, or any employment outcome.

Verify the transfer claim

Trace every reported behavior to the task opportunity, artifact, source trail, AI interaction where available, revision, and reviewer anchor. Sample reviewer agreement and report uncertainty. Compare participating and non-participating conditions only when the design supports that inference.

Reopen the ledger after changes to the work, model, data, training, reviewer, manager support, or measurement window. Report whether evidence is near, far, sustained, missing, or contradicted. Two Prune can provide task-bounded assessment evidence for human interpretation; it does not run workplace performance management or establish causal programme impact by itself.